import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

import plotly.express as px

df = pd.read_csv("data/NCRB_ADSI-2020_Table_1A.9.csv")

df = df[(df['Category']=='State') + (df['Category']=='Union Territories')]
df = df.sort_values(by='State/UT/City', axis=0)
df.head()

	Si. No.	Category	State/UT/City	Dangerous or Careless Driving/ Over-taking/etc. - Cases	Dangerous or Careless Driving/ Over-taking/etc. - Injured	Dangerous or Careless Driving/ Over-taking/etc. - Died	Over Speeding - Cases	Over Speeding - Injured	Over Speeding - Died	Driving under Influence of Drug/Alcohol - Cases	...	Other Causes - Died	Total Road Accidents - Cases	Total Road Accidents - Injured	Total Road Accidents - Died	Unmanned Railway Crossing Accidents - Cases	Unmanned Railway Crossing Accidents - Injured	Unmanned Railway Crossing Accidents - Died	Grand Total - Cases	Grand Total - Injured	Grand Total - Died
29	29	Union Territories	A & N Islands	82	90	8	26	31	2	33	...	0	141	145	14	0	0	0	141	145	14
0	1	State	Andhra Pradesh	3300	3630	1221	12344	13774	4778	154	...	668	17924	19675	7039	0	0	0	17924	19675	7039
1	2	State	Arunachal Pradesh	25	15	15	39	32	23	0	...	19	159	143	90	0	0	0	159	143	90
2	3	State	Assam	1169	893	462	3293	2530	1377	491	...	33	6737	5065	2813	1	0	1	6738	5065	2814
3	4	State	Bihar	2564	1936	1881	3674	3072	2785	152	...	150	8639	7019	6698	0	0	0	8639	7019	6698

5 rows × 51 columns

from urllib.request import urlopen
import json    

# with urlopen('https://raw.githubusercontent.com/Subhash9325/GeoJson-Data-of-Indian-States/master/Indian_States') as response:
#     counties = json.load(response)

with urlopen('https://raw.githubusercontent.com/nikhilkumarsingh/choropleth-python-tutorial/master/states_india.geojson') as response:
    counties = json.load(response)

print(counties["features"][0].keys())

# Note that the loaded geoJSON must have an outermost key id. Note key id is not present here

dict_keys(['type', 'geometry', 'properties'])

' Note that the loaded geoJSON must have an outermost key id. Note key id is not present here'

for i in range(len(counties['features'])):
    # Loop to add 'id' key
    counties['features'][i]['id']=counties['features'][i]['properties']['st_nm']
    
print(counties["features"][0].keys())

dict_keys(['type', 'geometry', 'properties', 'id'])

# print(counties['features'][0])
# print(counties['features'][0]['properties']['NAME_1'])
names = []
for i in range(len(counties['features'])):
    names.append(counties['features'][i]['id'])
# names.append('NA')
#     print(df)
# print(len(counties['features']))
# print(df['State/UT/City'])
names.sort()
df['name2geo']=names

df[['State/UT/City', 'name2geo']]

# df.head()

	State/UT/City	name2geo
29	A & N Islands	Andaman & Nicobar Island
0	Andhra Pradesh	Andhra Pradesh
1	Arunachal Pradesh	Arunanchal Pradesh
2	Assam	Assam
3	Bihar	Bihar
30	Chandigarh	Chandigarh
4	Chhattisgarh	Chhattisgarh
31	D & N Haveli and Daman & Diu	Dadara & Nagar Havelli
32	Delhi (UT)	Daman & Diu
5	Goa	Goa
6	Gujarat	Gujarat
7	Haryana	Haryana
8	Himachal Pradesh	Himachal Pradesh
33	Jammu & Kashmir	Jammu & Kashmir
9	Jharkhand	Jharkhand
10	Karnataka	Karnataka
11	Kerala	Kerala
34	Ladakh	Lakshadweep
35	Lakshadweep	Madhya Pradesh
12	Madhya Pradesh	Maharashtra
13	Maharashtra	Manipur
14	Manipur	Meghalaya
15	Meghalaya	Mizoram
16	Mizoram	NCT of Delhi
17	Nagaland	Nagaland
18	Odisha	Odisha
36	Puducherry	Puducherry
19	Punjab	Punjab
20	Rajasthan	Rajasthan
21	Sikkim	Sikkim
22	Tamil Nadu	Tamil Nadu
23	Telangana	Telangana
24	Tripura	Tripura
25	Uttar Pradesh	Uttar Pradesh
26	Uttarakhand	Uttarakhand
27	West Bengal	West Bengal

fig = px.choropleth_mapbox(df, geojson=counties, locations='name2geo', 
                           color='Dangerous or Careless Driving/ Over-taking/etc. - Died',
                           color_continuous_scale="Viridis",
                           mapbox_style="carto-positron",
                           zoom=3, center = {"lat": 20.5937, "lon": 78.9629},
                           opacity=0.5
                          )
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})

fig.show()